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All Levels

AI for Automotive Systems Engineering

Supercharge your automotive engineering practice with AI. Master high-speed R&D, automated manufacturing analysis, and AI-driven industrial modeling to deliver elite insights at 10x speed.

Note: Modules are fully customizable depending on specific customer needs and industry focus.

Duration: 3 to 5 days depending on the tailoring.
Certificate of Completion

What You'll Learn

  • Understand the fundamentals of AI in the automotive industry
  • Apply AI effectively across the automotive V-Model development process
  • Automate requirements engineering and management using AI
  • Accelerate ASPICE compliance implementations with intelligent tools
  • Enhance Functional Safety (ISO 26262) analysis through AI assistance
  • Rapidly prepare and valid prototypes with AI-driven engineering
  • Optimize testing, validation, and issue management workflows

Detailed Curriculum

Module 1
Introduction

Objective: Introduce the role of AI in modern automotive engineering workflows.
Topics:
▪AI vs Machine Learning vs Generative AI
▪Why AI is becoming critical in engineering
▪Challenges in automotive development
▪Overview of engineering lifecycle
▪Opportunities for AI automation
Practical Example: Using a large language model to analyse an engineering document.
Exercise: Generate structured summaries from technical specifications.

Module 2
Overview: Applying AI to the V-Model

Objective: Understand where AI can assist across the automotive development lifecycle.
Topics:
▪Automotive V-Model overview
▪Engineering phases in automotive development
▪Mapping AI capabilities to lifecycle phases
▪AI opportunities in system design, implementation, testing, and maintenance
Practical Exercise: Participants map AI opportunities across the V-Model.
Example outputs: requirement analysis, architecture support, test generation, issue triage

Module 3
AI for Requirements Management

Objective: Improve requirement quality and efficiency using AI.
Topics:
▪Requirement extraction from documents
▪Automatic requirement classification
▪Detecting ambiguity and inconsistencies
▪Traceability generation
▪AI-assisted requirement reviews
Practical Exercise: Use AI to detect ambiguous requirements, generate acceptance criteria, create traceability links.
Tools: LLM based tools or simple Python scripts.

Module 4
AI for Implementing ASPICE

Objective: Use AI to support Automotive SPICE compliant development processes.
Topics:
▪Overview of ASPICE process areas
▪Documentation burden in ASPICE projects
▪AI support for work products
▪AI support for process monitoring
Practical Exercise: Generate ASPICE work product templates using AI.
Example: AI assistance for SYS.2 System Requirements, SWE.3 Software Detailed Design, MAN.3 Project Management

Module 5
AI for Functional Safety

Objective: Support ISO 26262 activities using AI.
Topics:
▪Overview of functional safety lifecycle
▪Hazard identification support
▪AI-assisted HARA preparation
▪AI support for safety documentation
▪Safety case preparation
Practical Exercise: Use AI to generate hazard lists, safety goals, safety requirements.

Module 6
AI for Fast Prototyping

Objective: Accelerate early engineering experimentation.
Topics:
▪Rapid proof-of-concept development
▪AI-assisted coding
▪Code generation tools
▪Simulation support
Practical Exercise: Create a simple prototype using AI generated code.
Examples: signal processing algorithm, basic predictive model

Module 7
AI for Tests & Validation

Objective: Improve efficiency in test design and execution.
Topics:
▪Automatic test case generation
▪AI support for test coverage analysis
▪Test result analysis
▪Failure pattern detection
Practical Exercise: Use AI to generate test cases from requirements.
Example: Generate test cases for a safety feature.

Module 8
AI for Issue Management

Objective: Use AI to manage engineering issues effectively.
Topics:
▪Issue classification
▪Root cause identification
▪AI support for bug triage
▪Engineering knowledge extraction
Practical Exercise: Analyse issue tickets and cluster them automatically.
Examples: AI classification of defect type, subsystem impact, severity level

Interested in this course?

Contact us to get the full syllabus, pricing details, and discuss how we can tailor this for your team.

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Course Instructors

Learn directly from industry veterans with years of hands-on experience in automotive systems engineering and functional safety.

Dr. Sunish George Alumkal

Dr. Sunish George Alumkal

Principal Consultant | Systems Engineering

Industrial PhD in AI/ML with 20+ years in automotive & consumer electronics. He has built complex agentic systems for global brands, focusing on engineering precision and rapid industrial validation.